Translating garbage code creates even worse garbage code. I remember years ago a company I was with hired a company to translate COBOL to Java.

It was still crap when it was all over and the project was flushed. The biggest sin was the naming conventions used by the original code were maintained.

I've migrated code from one language to the other, and the best end results I've achieved were when I reverse engineered the original code and wrote the new code from scratch. It took longer, but in the process we discovered bugs that were then corrected in the new code. I loved it when the QA people would come back to me and say the results weren't the same, only to discover the original program had an undiscovered error.

I'm sure simple programs can be converted this way. My prediction is the big programs that no one wants to work on will still be crap code no one wants to work on after being converted. If they even run right.

https://www.cs.wisc.edu/2025/0...

And DARPA has a whole program for it:

https://www.darpa.mil/research...

Why is it that every Republitard answer to a problem is grift, corruption and ignoring the rule of law ?

Folks should visit the UK and find out what a real CCTV society looks like.

"cheap labor" They make it sound like their space program is based on thousands of people with shovels.

Based on the state of education, students relying on AI, who we vote for, etc I'd say in a decade or so we'll be a nation of morons.

The US can't stand the thought that we'd have to treat another nation as an equal and not as a client state.

Whether we like it or not, whether we use Windows or not, Microsoft is going to insist on adding the tax whether the PC comes shipped with Windows or not. This is illegal bundling, Microsoft has essentially ignored prior rulings, and it's pretty much a certainty the courts will be fine with this.

Investors are chasing a company that achieves some kind of AI singularity. Let's set aside the fact that there's no reason to believe there is anything but diminishing marginal returns by making marginal refinements to current frontier models. Let's imagine someone hits the jackpot and gets, not even AGI, but a system that's as far ahead of today's frontier model are ahead of 2020's GPT 2.0 in performance.

Globally AI revenues are 150 billion, against a cumulative burn rate of 450 billion. A model that is a generation ahead of others would almost certainly capture the lion's share of that revenue.

If AGI magically appears as a Sam Altman has promised investors it will, a hundred million is way too low. Add, maybe, another zero to the revenues.

Conservatively, a safer assumption is that frontier models will get marginally better based on refinements in training and reinforcement and the other bits and bobs that go into these systems. The nobody is winning the lion's share of anything, at least overnight. But you have to define "safe". By "safe" I mean unlikely to lose money. But some investors are clearly defining "safe" as "having the greatest chance of owning a piece of the biggest thing ever."

I'm not following this super-closely, but if Anthropic is pursuing adding multi-step model based reasoning to their system, that could be the basis of a generational leap in capability. But if that is an approach that looks like it has a chance of working, then their competitors are no doubt pursuing the same thing. In that case you'd expect the revenue pie to grow as the scope of model utilty increases, but that growth to be split among several competitors. This could credibly result in a revenue stream for some of them that is as big as the entire industry's revenue stream today. But there's going to be hell to pay on the data center impacts end of things.

"How did you get hired?"
"I was top at Fortnite. You?"
"Combat flight sims."

"Damn, two Boeings just crashed over an oil refinery on city limits and took out half the city."
"Did you remember to save your position beforehand?"
"Yeah."
"The reload and continue from there. No-one will notice."

I love computer games. I have XPlane 12 and many scenery packs. I rank well on Elite:Dangerous. From the sounds of it, the FAA would see me as over-qualified. In reality? There's no way in hell I'd be taking those kinds of risks with real lives. There's a huge difference between having good reflexes and a good eye, versus having the complex 4D spacetime relationship mental models needed for robust air traffic control.

You see, Linus has embraced transhumanism and is testing out the new kernel as a supplementary brain function, using MOSIX to offload all of the irritable comment generation at yet more nonsense on the mailing list to an Elizabot that he has written specifically to do this. This saves his actual brain for real work.

AI is not currently capable of performing any meaningful conceptual abstraction, it is only capable of very basic mechanical abstraction. Abstraction is itself multi-dimensional. Nor is there any indication that AI could ever perform multi-dimensional abstraction or multi-dimensional decomposition. Precious few humans are capable of it either, but some can.

No, AI would need humans because the best system is not a pure system but a hybrid system, and that means transhumanism with AI operating as an additional brain function rather than as a replacement for a brain.

Tool calling is, yes, but the current approach to it is (a) overt not transparent to the user, (b) not designed for the purpose I've outlined, and (c) not remotely good enough for the AI to be able to manage data through such tools.

Yes, you can connect an AI to a PostgreSQL database. Whoopee. Not even close to an AI detecting via classifiers that some of the data is relational in nature, transparently setting up its own PostgreSQL database in response, and using that proactively as an additional way to examine the data. You would need to explicitly set up the constructs, explicitly set up the databases, explicitly tell it when, where, and how to use the database, and if that particular usage conflicted with a way the AI actually worked, the AI would not be able to use it effectively. That's not remotely close to what I'm talking about.

What I'm talking about is much, much deeper than that. AIs lose focus when there is too much information currently in the system. Absolutely nothing stops an AI from using a document database as a virtual memory in which it can page in and out conceptual spaces so that the focus on any given step of a problem concerns just the problem. Other than such a concept not existing yet, which is kind of a limiting factor. But you need to know what connects to what. The AI could use an ontology reasoner for that, or a relational database, or an external graph. But you cannot provide those tools and you cannot configure them, for the simple reason that the AI is the only entity with any detailed map of how the underlying neural net is connecting those ideas up.

The AI itself has to select the tools, has to configure the tools, has to do all the work. Which means YOU cannot use any sort of API. YOU should have no involvement in the underlying mechanisms needed for the internal NN housekeeping.

And that's your other error. You're assuming this is about user data. No. It's about housekeeping operations within the NN itself to maximise functionality, it has nothing to do with the user side of things at all. If the user was capable of setting up dynamically structured databases that mutated with each step of a decomposed analysis, you'd have done everything the AI would be doing and you wouldn't need the AI to begin with.

You cannot do this work, you cannot even assist in this work, it has to be dynamic and it has to be utterly invisible to the operator.

We don't need bigger models, at this point. What we need is multi-dimensional decomposition, problem space transforms, and the ability for AI to use external tools (such as SQLite, memcached, etc) so that it can externalise static data that it needs to not corrupt accidentally but still keep in easy access.

If we had that, most of the things "bigger models" will do will actually end up being done better, quicker, with fewer compute resources.

To be useful in deep space, you're going to have to deal with very harsh radiation, far harder than the Vikings dealt with. You really want to have some large number of computers, where the number has to be odd and exceed 5. The reason it has to exceed 5 is that you need 5 in order to be able to use the Byzantine General's Problem to discern which computers are working correctly and which are radiation damaged. Since some will be damaged over time and you want 5 computers still operating around the time the power runs out, you have to start with more than that.

Massively radiation-hardened highly-robust low-power chips could be done. If you were clever enough, you could do this as a wafer-scale system where you marked parts as bad and networked around them. Free space optical communication across the wafer might be doable.

They might well still end up being low-density CMOS discrete logic.

Lead-lining the electronics isn't as much of a problem if you've launchers capable of handling heavy objects and don't mind burning through a lot of ion drive propellant for course corrections. This would improve the hardening beyond what can be done through the usual operations.

But, yeah, you'd need defect-free microelectronics. You really can't handle F00F bugs or defective instructions once you pass Earth orbit. And I can't think of anyone who knows how to do that.